We first assess whether sentence encoders yield coherent representations of meaning by computing the Spearman correlation between the human ratings present in the sick benchmark [1]} and the cosine and Euclidean distances between the two corresponding sentence embeddings; as with word embedding spaces in Section , we expect a significant anti-correlation between the two.
Figure REF  summarizes correlation scores for Skip-Thought [2]}, Infersent [3]} and the Universal Sentence Encoder [4]}, along with a randomly initialized (and untrained) Transformer [5]} for perspective.
We observe that use yields the most consistent semantic representations and thus decide to focus in the following on this particular model.
